vaccines and immunoinformatics approaches

THLANet: A Deep Learning Framework for Predicting TCR-pHLA Binding in Immunotherapy Applications

THLANet predicts TCR-pHLA binding using deep learning, outperforming existing methods and providing structural insights into TCR-antigen interactions.

Chasing the Bigfoot: shared molecular patterns among pMHC-I differentiate self from non self peptides

This study uses structural and physicochemical features of pMHC complexes to distinguish self from non-self peptides, achieving strong classification performance.

Design of a Multi-Epitope Vaccine using β-barrel Outer Membrane Proteins Identified in Chlamydia trachomatis

The paper designs a multi-epitope vaccine using β-barrel outer membrane proteins from Chlamydia trachomatis, showing it is antigenic, non-allergenic, and capable of strong immune response through TLR4 binding.

Integrative reverse vaccinology approach for identification of a vaccine antigen candidate with immunoprotective and anti-fibrotic potential against Schistosoma japonicum-associated liver fibrosis

The paper identifies a vaccine antigen candidate, DRE2_SCHJA, with potential to prevent and treat Schistosoma japonicum-associated liver fibrosis using an integrative computational approach.

Design of a novel multi-epitope mRNA vaccine against BtHKU5-CoV-2 using immunoinformatics

This paper designs a multi-epitope mRNA vaccine against BtHKU5-CoV-2 using immunoinformatics, showing strong antigenicity, immunogenicity, and potential to trigger robust immune responses.

In Silico Tool for Predicting and Scanning Rheumatoid Arthritis-Inducing Peptides in an Antigen

This paper develops models to predict RA-inducing peptides using machine learning, deep learning, and large language models, achieving high AUC scores and implementing them in a web tool.

Plasmodium circumsporozoites targeted by RFdiffusion / cyclic-peptides

The paper computationally designs cyclic peptides targeting Plasmodium falciparum CSP to improve malaria vaccine efficacy.

Comprehensive mapping of identical epitopes across human proteins reveals implications for immune recognition and therapeutic design

The paper maps shared epitopes across human and murine proteins to address immune recognition challenges and provides a web server for epitope detection.

Structural Homology and Electrostatic Potential Comparisons of Epitope Pair Candidates for Molecular Mimicry Triggering of Type 1 Diabetes Mellitus

The paper compares sequence and structural homology of epitope pairs to identify molecular mimicry candidates for type 1 diabetes, finding most pairs show structural similarity.

Training data composition determines machine learning generalization and biological rule discovery

This study shows that the composition of training data, especially negative examples, affects machine learning model performance and biological rule discovery in antibody-antigen binding.

Antibody immunogenicity prediction and optimization with ImmunoSeq

ImmunoSeq predicts antibody immunogenicity by analyzing k-mer peptides from self and non-self libraries, identifying immunogenic hotspots and optimizing sequence humanness.

VaccineDesigner: A Web-based Tool for Streamlined Epitope-based Vaccine Design

VaccineDesigner is a web-based tool that streamlines multi-epitope vaccine design by integrating epitope prediction and evaluation methods.

Reengineering the antigen optimization process for superior neoantigen vaccine design

AlphaVacc optimizes neoantigen peptides using deep reinforcement learning, improving binding affinity and T cell response in experimental validation.

A structure-informed evolutionary model for predicting viral immune escape and evolution

This paper introduces KEScape, a deep learning model that predicts viral immune escape and evolution by integrating structural information and prioritizing impactful mutations, outperforming existing models.

Molecular characterization of the archaic HLA-B*73:01 allele reveals presentation of a unique peptidome and skewed engagement by KIR2DL2

The paper characterizes HLA-B*73:01, revealing its unique peptide binding motif, restricted peptidome, and distinct interaction with KIR2DL2, suggesting evolutionary advantages in pathogen resistance.

HLAIIPred: Cross-Attention Mechanism for Modeling the Interaction of HLA Class II Molecules with Peptides

HLAIIPred predicts HLAII-presented peptides using a Transformer-based model, outperforming existing methods and identifying core interaction residues.

An improved deep learning model for immunogenic B epitope prediction

This study improves B epitope prediction by combining protein embeddings with structural features, showing better performance than models using embeddings alone.

Sliding Window INteraction Grammar (SWING): a generalized interaction language model for peptide and protein interactions

SWING is a novel interaction language model that predicts peptide:MHC interactions and protein-protein interaction disruptions using sequence data.

Design of high specificity binders for peptide-MHC-I complexes

The paper designs high specificity binders for peptide-MHC-I complexes using deep learning, achieving specific targeting of pMHCs and enabling T-cell activation.

De novo designed pMHC binders facilitate T cell induced killing of cancer cells

This paper designs de novo pMHC binders using generative models to create T cell-based cancer therapeutics with high specificity and minimal cross-reactivity.

PathMHC: a workflow to selectively target pathogen-derived MHC peptides in discovery immunopeptidomics experiments for vaccine target identification

PathMHC improves identification of pathogen-derived MHC peptides for vaccine targets by combining computational analysis with targeted mass spectrometry.

The antigen presenting molecule MR1 binds riboflavin catabolites

This study identifies riboflavin catabolites that bind MR1, reduce its cell surface expression, and inhibit MAIT cell activation.

NetTCR-struc, a structure driven approach for prediction of TCR-pMHC interactions

The paper introduces NetTCR-struc, a GNN-based method to improve TCR-pMHC interaction modeling by enhancing docking quality scoring and structural model selection.

HAIRpred: Prediction of human antibody interacting residues in an antigen from its primary structure

HAIRpred predicts human antibody-interacting residues in antigens using machine learning and features like evolutionary profiles and surface accessibility, achieving an AUC of 0.72.

In silico tool for Predicting, Designing and Scanning IL-2 inducing peptides

The paper develops a tool to predict, design, and scan IL-2-inducing peptides using machine learning, deep learning, and ensemble methods, achieving high accuracy on multiple datasets.

Computational Prioritization of T Cell Epitopes to Overcome HLA Restriction and Antigenic Diversity in Plasmodium falciparum

This paper develops a computational tool to identify conserved, immunogenic T cell epitopes across Plasmodium falciparum strains and HLA alleles, improving malaria vaccine design.

Self-iterative multiple instance learning enables the prediction of CD4+ T cell immunogenic epitopes

ImmuScope predicts CD4+ T cell immunogenic epitopes using self-iterative multiple-instance learning, improving accuracy with multi-allelic and single-allelic data.

Epi4Ab: A data-driven prediction model of conformational epitopes for specific antibody VH/VL families and CDR H3/L1 sequences.

Epi4Ab predicts conformational epitopes using antibody VH/VL families and CDR H3/L1 sequences to identify in-contact antigen residues.

Learned Geometry, Predicted Binding: Structurally-BasedPrediction of Peptide:MHC Binding Using AlphaFold 3 EnablesCD4 T Cell Epitope Prediction

The paper introduces MHCIIFold-GNN, a method combining structural modeling and graph neural networks to predict CD4 T cell epitopes, achieving state-of-the-art performance.

In silico design and computational characterization of novel chimeric multiepitope antigens for Mpox serosurveillance

This paper designs and computationally validates two novel multiepitope antigens for Mpox serosurveillance using immunoinformatic approaches and structural analysis.